Safwan Wshah

Assistant Professor 351B Votey Department of Computer Science Office: +1-(802) 656-8086 University of Vermont E-mail: [email protected]

Research Machine Learning, Image and Video Processing, Deep Learning, Pattern Recognition, Interests Computer Vision, Document Imaging, Digital Signal Processing, Control Systems and Computer Architecture.

Education State University of NewYork at Buffalo, Buffalo, NY USA PhD, Department of Computer Science and Engineering, May 2008 to June 2012.

University of Cincinnati, Cincinnati, OH USA PhD Student, Department of Electrical and Computer Engineering, Sep 2007 to May 2008.(Transfered to State University of NewYork at Buffalo )

The University of Jordan, Amman, Jordan MSc. in Communication engineering, Department of Electrical Engineering, Sep 2002 to Jul 2005.

Princess Sumaya University, Amman, Jordan BSc. in Electronics engineering, Department of Electronics Engineering, Oct. 1996 to Feb. 2001.

Doctoral Title: Word Spotting in Multilingual Handwritten Documents. Dissertation Advisor: Prof. Venu Govindaraju, SUNY Distinguished Professor.

A new approach has been implemented for keywords spotting in multilingual handwritten documents based on statistical Markov models. The approach is script independent scal- able over many languages such as English, Arabic and Devanagari and has many applica- tions in information retrieval and indexing including language identication of handwritten documents.

Research Experience University of Vermont, Burlington, VT, USA August 2017 to Present Assistant Professor, Department of Computer Science

PARC - A Xerox Company, Webster, NY, USA June 2014 to June 2017 Research Scientist

Creating new machine learning and image processing algorithms in computer vision and document imaging fields for transportation, healthcare and education.

• Vehicle Passenger Detection System: I Implemented scalable Deep Neural Net- works algorithms for counting passengers in vehicles. Xerox Vehicle Passenger De- tection System identifies the number of occupants in a vehicle with more than 95% accuracy, at speeds ranging from stop and go to 100 mph. I created scalable deep learn- ing convolutional neural network algorithms to count the number of passengers inside the car, for more information refer to Xerox-Vehicle-Passenger-Detection-System.

• Digital Alternatives: I Implemented high adavnce image processing algorithms to analyis documents captured from different sources (mobile camera, electronic,

1 of 8 scanned, etc) in order to fill them electronically on fly. The Xerox Digital Alter- natives Tool maintain productivity and reduce document workflow complexity in an always-connected world. It is a workflow solution supporting todays increasingly mo- bile knowledge worker population, providing the ability to complete multiple workflows within a single application and without the need for paper.For more information refer to Xerox Digital Alternatives.

• Xerox Ignite Educator Support System: I Implemented image processing and deep learning approaches based on auto-encoders and convolutional neural networks to recognize students handwriting from elementary schools. Ignite is a workflow and software solution that is using the power of data to transform K-12 education. Teacher would first scan students homework and/or exams into the Ignite system via a range of multifunctional input devices. Xerox Ignite reads, interprets, and analyzes the students work in minutes. For more information about the project refer to Ignite Educator Support System.

• Human Video analytics: In this on-going project we are building a set of algo- rithms for human activities recognition in surveillance cameras, algorithms include detection, tracking and action recognition.

• Surgical Video Analytics: This Project is a collaboration with University of Rochester Medical Center (URMC) in which we Implemented Deep Neural Networks and BoW algorithms for action quality assesment. Videos captured in-vivo during a surgical procedure are often post-analyzed in order to evaluate the quality of the procedure, identify errors that have taken place, assess the expertise and skill level of the surgeon, and to provide coaching and feedback to students of surgery. This analysis is currently done manually, requiring many hours of laborious inspection by expert surgeons.

Xerox Research Center, Webster, NY, USA August 2012 to May 2014 Research Scientist

Creating new machine learning and image processing algorithms in computer vision and document imaging fields for transportation, healthcare and education.

• Form Registration:I Implemented image processing algorithms for independent global and local form registration for health care transaction processing. • Crowd Sourcing for Medical Forms: I Developed image processing modules to process different medical forms with emphasis on their crowdsourcability. • Statistical Toolkit: Develop high level statistical toolkit (C/C++) that implements statistical analysis and machine learning functionalities such as Neural network, con- volutional neural networks. • Expression Spotting System: I Implemented deep learning algorithms to perform a low level of information retrieval that detect and recognize specific information such as mail address, email address, phone number, dates, numerical tables, page number, etc... • Handwriting and Machine Printed Text Separation : I participated in imple- menting deep learning algorithms that separate handwritten from machine printed text in structured documents using auto-encoders.

Center for Unified Biometrics and Sensors, University at Buffalo, Buffalo, NY USA Research Assistant May 2008 to Sep. 2010

• Keyword spotting in off-line Handwritten Documents : I Proposed filler and background models for keyword spotting that combines local scores and global word hypotheses scores to learn a classifier for keywords and non-keywords based on statistical Markov models..

• Arabic Handwritten Recognition: I Developed features and statistical ap-

2 of 8 proachs to recognize full Arabic handwritten documents without line segmenta- tion. The method used n-gram language modeling for enhancing the results. In this project I developed advanced technique based on continuous probability-Connected Hidden Markov Models with robust features such as GSC (Gradient-Structural- Concavity ) features to gain the best performance on full Arabic document. The developed algorithm alleviated the need for segmenting Arabic text prior to recog- nition. The algorithm performed concurrently both segmentation and recognition, the algorithm achieved 70% accuracy on the public AMA dataset.

Teaching Experience Primary instructor

Adjunct Professor, University of Rochester, Electrical and Computer Engineer- ing Department (Fall-2016): Teaching Digital Signal Processing course as primary instructor. The course had 45 students from graduate and undergraduate levels from Electrical and Biomedical Engineering Departments.

Internal course, PARC (Fall-2015): Teaching Deep Learning in Computer Vision course for more than 50 engineers and researchers from different backgrounds.

Teaching Assistant

Introduction to C++, Fall-2007 and Numerical Analysis using Matlab, Spring-2008. My responsibilities included holding office hours, grading exams and homework, writ- ing and grading quizzes and assigning grades.

Software Engineering, Fall-2010 and Fall-2011: My responsibilities included partic- ipating in course design, suggesting and grading projects and writing and grading exams.

Computer Architecture, Spring-2011 and Spring-2012: I Handled the project part which worth one-third of the course total grades. My responsibilities included prepar- ing the project material, project lectures, assignments, supervising and helping stu- dents during office hours and grading.

Mentoring During my professional career as researcher at PARC and Xerox research labs I mentored many senior graduate students during their summer internships. Select the research area, hire students with the right skills through series of interviews, setup the needed resources, guide interns on how to conduct research and help in publishing their work.

Industry Experience Xerox, Webster, NY, USA Software Engineer, Internship May 2011 to Sep. 2011

Form Registration: Built a full frame work to register forms globally and locally by devel- oping machine learning pattern matching algorithm, the algorithm showed high accuracy for many popular forms such as medical HCFA and UB forms.

Applied Media Analysis, Collge Park, MD, USA Software Engineer, Internship May 2010 to Sep. 2010

Research and development of software for Arabic Optical Character Recognition using continuous probability-Connected Hidden Markov Models. The developed algorithm alle- viated the need for segmenting Arabic text prior to recognition. The algorithm performed concurrently both segmentation and recognition.

3 of 8 Copanion, Andover, MA, USA Software Engineer, Internship May 2009 to Sep. 2009

Development of advanced convolutional neural network algorithms to recognize English handwritten text used for tax form recognition.

Lead Technologies , Amman, Jordan Software Engineer Jan. 2001 to Sep. 2007 Development and Research at different levels:

• Research in Image, Document, and Video processing algorithms. • Implementing the developed algorithms under C, C++, Java, and .Net. • Involved in training and guiding new members and updating work guidelines. • Team leader (9 Team members), managing and supervising all team projects.

Master Thesis Title: A Robust Algorithm for Face Detection in Color Images Based on Color Segmen- tation and Neural Network Techniques. Development of a full system to detect and recognize multi faces in 2D images. a new approach for face detection have been developed in color images in the presence of varying light conditions as well as complex background based on light control, skin detection and color segmentation techniques. Possible face features such (eyes, and mouth) are recog- nized inside detected skin regions using neural networks. Experimental results demon- strate successful face detection and recognition over a wide range of facial variation in color, position, scale, orientation, 3D pose, and expression in images from several public datasets.

Main courses • during Undergraduate : Computer organization and design , Electronics, Advanced electronics, Electronics circuits, Advanced electronic circuits, Microprocessor system, Microprocessor interface, Electromagnetic, Electric machinery, Power systems, Power electronics, Analog and digital communication, Industrial instruments, Advanced In- dustrial instruments, Electronic instruments, Bio-measurements and instruments, C language, Automatic control and Digital signal processing.

• during Master : Communication systems, Electromagnetics, Random process theory, Fiber optics,Communication networks (Markov chains, Markov Processes, Queuing Theory and Applications), Digital signal processing and Digital Control Systems.

• during PhD : Artificial intelligent, Computer Vision and Image processing, Biometric and Image analysis, Pattern recognition, Algorithms analysis and design, Intelligent systems (artificial neural networks), Random and stochastic processes, Applied sys- tems theory (Modern Control and Linear System theory), Modern network concepts, Theory of computation (Turing machine), Biomorphic Systems (embodied robotics), Operating systems and Computer Architecture.

Journal S. Wshah, G. Kumar , V. Govindaraju, Statistical script independent word spotting in articles offline handwritten documents, Pattern Recognition Journal, 2014. S. Wshah, I. Mansour, A Robust Algorithm for Face Detection in Color Images Based on Color Segmentation and Neural Network Techniques, Dirasat, University of Jordan, Engineering Science, Volume 33, No. 2, 2006.

Peer-Reviewed Conference Publications 4 of 8 S. Wshah, B. Xu, O. Bulan, J. Kumar, P. Paul, Deep learning architectures for do- main adaptation in HOV/HOT lane enforcement, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV 2016). B. Xu, O. Bulan, J. Kumar, S. Wshah, V. Kozitsky, P. Paul, Comparison of Early and Late Information Fusion for Multi-camera HOV Lane Enforcement , IEEE 18th International Conference on Intelligent Transportation Systems, (ITSC 2015). E. Gross, S. Wshah, I. Simmons, G. Skinnerl, A handwriting recognition system for the classroom, Fifth International Conference on Learning Analytics And Knowledge, (LAK 2015). O. Bulan, S. Wshah, R. Palghat, V. Kozitsky, A. Burry, USDOT Number Localization and Recognition From Vehicle Side-View NIR Images , IEEE Conference on Computer Vision and Pattern Recognition Workshops, (CVPR 2015). G. Kumar , S. Wshah ., G.Venu , Variational dynamic background model for keyword spotting in handwritten documents, Electronic Imaging. International Society for Op- tics and Photonics, (IS&T/SPIE 2013). G. Kumar , S. Wshah ., G.Venu , Segmentation-free keyword spotting framework using dynamic background model, In proceeding of: Document Recognition and Retrieval XX, (DRR 2013). S. Wshah., G. Kumar G., G. Venu , Multilingual Word Spotting in Offline Handwritten Documents , 21st International Conference on Pattern Recognition, (ICPR 2012). S. Wshah ., Kumar G., Venu G., Script IndependentWord Spotting in Offline Handwrit- ten Documents Based on Hidden Markov Models, International Conference on Frontiers in Handwriting Recognition, (ICFHR 2012). S. Wshah, G.Venu , C. Yanfen , L. Huiping , A Novel Lexicon Reduction Method for Arabic Handwriting Recognition, International Conference on Pattern Recognition, (ICPR 2010). S. Wshah , S. Zhixin , G. Venu , Segmentation of Arabic Handwriting Based on both Contour and Skeleton Segmentation, Conference on Document Analysis and Recogni- tion (ICDAR 2009). S. Wshah , I. Mansour , (2005). A Robust Algorithm for Face Detection in Color Im- ages, IASTED International conference on visualization, imaging, and image processing (2005), Spain, (VIIP 2005).

Abstracts T. Osinsk, D. Arpit, S. Wshah, Ahmed Ghazi, Computer-generated assessment of technical surgical skills (CATS) , American Urological Association Annual Meeting, (AUA2016).

Patents S. Wshah, B. Xu, O. Bulan, MULTI-LAYER FUSION IN A CONVOLUTIONAL NEU- RAL NETWORK FOR IMAGE CLASSIFICATION, 20151361US02 , Filed on 10 Jun 2016(Pending). S. Wshah, B.i Xu, O. Bulan, SYSTEM AND METHOD FOR EXPANDING AND TRAINING CONVOLUTIONAL NEURAL NETWORKS FOR LARGE SIZE INPUT IMAGES, 20160110US01, Filed on 28 Jun 2016(Pending). S. Wshah, R. Bala, D. Arpit, METHOD AND SYSTEM FOR EVALUATING THE QUALITY OF A SURGICAL PROCEDURE FROM IN-VIVO VIDEO, 20150962US02, 26 Apr 2016 (Pending). S. Wshah, M. Maltz, D. Venable, METHOD AND SYSTEM OF IDENTIFYING FILL- ABLE FIELDS OF AN ELECTRONIC FORM, 20151112US01, 10 Dec 2015 (Pending). M. Maltz, S. Wshah, BUILDING TABLES WITH ROW AND COLUMN HEADING FROM A SCANNED FORM, 20150435, 09 Jul 2015 (Pending)

5 of 8 M. Maltz, S. Wshah, RELATIONAL DATA BASE FOR FORM UNDERSTANDING WITH ORPHAN REMOVAL, 20150436, 24 Jul 2015 (Pending)

Balamurugan, L. Stone,M. Samptha, R. Taylor, S. Wshah, METHOD AND SYSTEM FOR COST OPTIMIZED CROWDSOURCING BASED ENTERPRISE FORM DIG- ITIZATION, 20150052, 01, March 2015(Pending) S. Wshah , M. Campanelli , METHODS AND DEVICES FOR FORM-INDEPENDENT REGISTRATION OF FILLED-OUT CONTENT, US Patent US Patent App. 14/196,108, 2014. R. Eschbach , S. Wshah ,ALTERING SCANS TO INCLUDE SECURITY FEATURES IDENTIFYING SCAN ORIGINATION, US Patent 9,258,452, 2014. E. Gross, G. Skinner, S. Wshah , Isaiah L Simmons , CONFIRMING AUTOMATI- CALLY RECOGNIZED HANDWRITTEN ANSWERS, US Patent App. 14/627,457, 2014. S. Wshah ,M. Campanelli , Y. Zhou , METHOD AND APPARATUS FOR CLASSIFY- ING MACHINE PRINTED TEXT AND HANDWRITTEN TEXT, US Patent App. 14/284,592, 2014.

S. Wshah , M. Campanelli , CHARACTER RECOGNITION METHOD AND SYS- TEM USING DIGIT SEGMENTATION AND RECOMBINATION, US Patent App. 15/149,483,2013. S. Wshah ,M. Campanelli , GLOBAL REGISTRATION OF FILLED-OUT CONTENT IN AN APPLICATION FORM, US Patent App. 15/149,483, 2013.

Honors & Awards • PARC Special Award for Contributions to the Most Innovative Project. 2015 • Graduate Teaching Assistantship, University at Buffalo, Sep. 2010 - May 2012. • Graduate Research Assistantship, University at Buffalo, May 2008 - Sep. 2010. • Graduate Research Assistantship, University of Cincinnati, Jan 2008 - May 2008. • Graduate Teaching Assistantship, University of Cincinnati, Sep. 2007 - Jan 2008.

Invited Talks Opportunities and Challenges of Machine Learning in Real-World Applications (Health care, Transportation and Education), University of Rochester, Electrical and Computer Engineering Department, Oct. 2016.

Deep Learning in Real-World Applications, University at Buffalo, Center for Unified Biometrics and Sensor (CUBS), August. 2016.

Attended CVPR Computer vision and Pattern Recognition LAS VEGAS, USA, 2016. Conferences CVPR Computer vision and Pattern Recognition Portland, USA, 2015. CVPR Computer vision and Pattern Recognition Boston, USA, 2014.

ICDAR Conference on Document Analysis and Recognition, Washington, DC, 2013. CVPR Computer vision and Pattern Recognition Portland, USA, 2013. ICPR International Conference on Pattern Recognition, Istanbul, Turkey, 2010.

ICDAR Conference on Document Analysis and Recognition, Spain July 23-27, 2009.

6 of 8 Program IEEE International Symposium on Multimedia (ISM2013). Committee ICDAR Conference on Document Analysis and Recognition, Nancy, France, 2015.

Program Chair, 5th International Workshop on Multilingual OCR, Nancy, France, 2015. The 2017 International Conference on Advanced Technologies Enhancing Education (ICAT2E2017), 2017 The Seventh International Conference on Performance, Safety and Robustness in Complex Systems and Applications(PESARO 2017), 2017

Reviewer for International Journal of Pattern Recognition and Artificial Intelligence Journals Signal Image and Video Processing The Journal on Electronic Imaging The Institution of Engineering and Technology- Image processing

International Journal on Document Analysis and Recognition

professional IIEEE (Institute of Electrical and Electronics Engineers) – Signal Processing Society memberships Technical Platforms: Windows, UNIX Skills Programming Languages and toolkits: C, C++, Caffe, Torch, Matlab, Python, OpenCV, .Net (C#, VB), Java, VB, SQL, Assembly, verilog. Scripting Language: Shell Scripts, Perl. Web Technologies : HTML, JavaScript, VBScript. Tools : DirectX, Nvidia GPU processing.

7 of 8 References Dr. Venu Govindaraju Vice President for Research and Economic Development SUNY Distinguished Professor The State University of New York at Buffalo Relationship: PhD Supervisor Address: 113 Davis Hall, Buffalo NY 14260-2500 Phone: +1-716-645-1558 Email: govind@buffalo.edu Website: http://cubs.buffalo.edu/govind

Dr. David Doermann DARPA (Program manager) Senior Research Scientist, Director LAMP University of Maryland Relationship: I interacted with him on different projects during my PhD studies and continued to do while working at PARC. Address: 3451 AV Williams Building, College Park, MD 20742 Phone: +1-301-405-1767 Email: [email protected]

Dr. Walt Johnson Former Vice President, PARC PARC, a Xerox Company Address: 800 Phillips rd webster ny Relationship: Manager at PARC Email:[email protected]

Dr. Jason J. Corso Associate Professor Electrical Engineering and Computer Science University of Michigan Relationship: Professor and top researcher in computer vision. Address: 4227 EECS, 1301 Beal Ave, Ann Arbor, MI 48109 Phone: +1-734-647-8833 Email:[email protected]

Dr. Mark Bocko Distinguished Professor, Electrical and Computer Engineering Professor, Physics & Astronomy Chair of the Department of Electrical and Computer Engineering University of Rochester Relationship: Department Manager, teaching reference at University of Rochester. Address: 518 CSB, 120 Trustee Rd, Rochester, NY 14620 Phone: +1-585- 275-4879 Email:[email protected]

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